Wind noise is a well-known problem that is often encountered when trying to estimate acoustic signal parameters such as directions of arrival and waveforms. Significant signal-to-noise ratio (SNR) improvements are often obtained by using mechanical windscreens, and the performance of several types and shapes of windscreens have been investigated over the years. In some applications, mechanical windscreens may be adequate for reducing the overall measured level of pressure fluctuations due to wind noise without significantly distorting the acoustic energy. However, in other applications, these techniques may be inadequate, and the correlation among fluctuations due to wind noise can bias the estimates of the direction of arrival of the acoustic energy and the corresponding waveform.
When continuous wave (CW) signals are present, gains in SNR can be achieved by time averaging to improve detection abilities. Further, when detecting the direction of arrival is important, sensor arrays can be exploited to enhance SNR by spatial averaging through beamforming. In spatial averaging, sensors are frequently assumed to be spaced far enough apart so that the wind noise is not correlated from sensor-to-sensor. If this assumption is not met, biased estimates of the signal parameters will be produced.
In applications involving transient acoustic signal detection, however, time averaging is generally ineffective at improving SNR. In such cases, mechanical windscreens and spatial averaging are generally utilized. In order to achieve the desired estimation performance, an appropriate number and spatial configuration of sensors is used. As in continuous wave signal detection, for at least some known beamforming systems, it is important in that the sensors are spaced far enough apart to avoid significant correlation of wind noise. Incidentally, if the wind noise is correlated and its correlation structure is known at the time the transient acoustic signal is acquired, modified beamforming techniques can be used to reduce bias. However, wind noise is frequently highly non-stationary (gusty), and therefore, in at least some known acoustic detection systems, it is relatively difficult to determine the correlation structure of the wind noise before acquiring data.
Further, wind noise problems are often exacerbated at infrasonic frequencies and/or audible frequencies on moving platforms, including ground vehicles and unmanned aerial vehicles. That is, at least some known acoustic microphone systems operating on mobile vehicles suffer from flow noise in the audible range when the vehicles are moving at typical operating speeds. In these applications, mechanical windscreens may provide only limited benefits.